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from transformers.configuration_utils import PretrainedConfig
class LlamaMoEConfig(PretrainedConfig):
model_type = "llama_moe"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
pretraining_tp=1,
tie_word_embeddings=False,
rope_scaling=None,
# -------- moe expert configs --------
num_experts=16,
num_selects=4,
size_experts=None,
# -------- moe gate configs --------
gate_type="TopKBalancedNoisyGate",
gate_network="mlp",
gate_use_softmax=True,
gate_use_balance=True,
gate_balance_loss_weight=1e-2,
gate_add_noise=True,
# TopKBalancedNoisyGate
gate_noise_epsilon=1e-2,
# -------- moe calculator configs --------
calculator_type="UniversalCalculator",
multiply_gate_scores=True,
score_scale_factor=1.0,
add_weight_norm=False,
# SwitchDropTokenCalculator
drop_tokens=True,
dropped_padding="zero",
capacity_factor=1.25,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.pretraining_tp = pretraining_tp
self.use_cache = use_cache
self.rope_scaling = rope_scaling
self._rope_scaling_validation()
self.num_experts = num_experts
self.num_selects = num_selects
self.size_experts = size_experts
self.gate_type = gate_type
self.gate_network = gate_network
self.gate_use_softmax = gate_use_softmax
self.gate_use_balance = gate_use_balance
self.gate_balance_loss_weight = gate_balance_loss_weight
self.gate_add_noise = gate_add_noise
self.gate_noise_epsilon = gate_noise_epsilon
self.calculator_type = calculator_type
self.multiply_gate_scores = multiply_gate_scores
self.score_scale_factor = score_scale_factor
self.add_weight_norm = add_weight_norm
self.drop_tokens = drop_tokens
self.dropped_padding = dropped_padding
self.capacity_factor = capacity_factor
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
def _rope_scaling_validation(self):
"""
Validate the `rope_scaling` configuration.
"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
raise ValueError(
"`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
f"got {self.rope_scaling}"
)
rope_scaling_type = self.rope_scaling.get("type", None)
rope_scaling_factor = self.rope_scaling.get("factor", None)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
)
if (
rope_scaling_factor is None
or not isinstance(rope_scaling_factor, float)
or rope_scaling_factor <= 1.0
):
raise ValueError(
f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"
)
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